get_predictive_signals

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are empty, so the description bears full burden. It reveals statistical validation methods, time horizons, and status categories, but does not mention side effects, authentication needs, data recency, or whether results are cached or real-time. Some transparency is provided, but gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured. The key information is front-loaded ('Statistically validated leading indicator signals evaluated against live supply chain data'). Every sentence adds unique value, covering statistical basis, output format, organization, and use cases without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and no output schema, the description provides substantial context: statistical thresholds, output statuses, signal count and tiers, predictor groups, and target users. It could have described the exact return structure (e.g., JSON fields), but overall it is fairly complete for an agent to understand the tool's output and usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters, and schema description coverage is 100% trivially. With 0 parameters, the baseline score is 4. The description does not need to add parameter semantics, and it correctly omits parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns statistically validated leading indicator signals with specific statistical criteria (Granger-causal, p<=0.01, directional accuracy >=55%). It details output statuses (ACTIVE, WATCH, CLEAR) and organization across 58 signals and 3 tiers. This specificity goes beyond a simple verb+resource and adequately distinguishes from sibling monitoring tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists target users (commodity traders, procurement teams, hedge funds) and use cases (forward-looking positioning, buy/defer timing, alternative data). While it does not explicitly state when not to use or name alternatives, the context provided is strong enough to guide appropriate tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.